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An EM algorithm for linear distortion channel estimation based on observations from a mixture of Gaussian sources

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1 Author(s)
Yunxin Zhao ; Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA

In this work, an expectation maximization (EM) algorithm is derived for maximum likelihood estimation of the autocorrelation function of a linear distortion channel as well as the level of additive noise, under the assumption that the source signal comes from a mixture of Gaussian sources. To facilitate parameter initialization in the EM algorithm, a correlation-matching based estimation algorithm is developed for the channel autocorrelation function. The proposed EM algorithm was evaluated on speech-derived simulated data of multiple autoregressive Gaussian sources and real speech of isolated digits under signal-to-noise ratios (SNRs) of 20 dB down to 0 dB. The algorithm is shown to produce convergent estimation results as well as estimates of signal statistics that lead to significantly improved classification accuracy under additive and convolutive noise conditions

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Speech and Audio Processing, IEEE Transactions on  (Volume:7 ,  Issue: 4 )